Function passed to optimization routine to minimize to
estimate parameters. Uses mean squared error to calculate
difference between dataResponse and what
computeModel) would forcast for
dataX using parameters pars.
modelObjectiveFunction(pars, dimension, dataX,
dataResponse, responseFunction = calculateResponse,
sessionBoundaries = NA, fitG = TRUE)Vector of parameters mFast, mSlow, n, hSlow, and r
What dimension to return error in, 1 for single criteria optimization, or number of columns of data for multicriteria optimization
List of observations of process x(i) (with real time)
Corresponding list of observations of subject's response to x(i), i.e. ~x(i)
The function to use to transform the forecast into a response
(option) Vector defining how to
group the trials into sessions where the items are the
starting indicies for each session (so the last value can
be the index after the last trial) and NAs are
used for gaps between sessions
TRUE to estimate g, or FALSE to
fix g at 0
Error between dataRespones and what would have
been estimated for dataX based on parameters pars